Data science and Data preparation with KNIME [Video]
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Free ChapterData Science and Data Preparation with KNIME
- Course Introduction
- Reading Multiple CSV Files in Bulk into KNIME Update
- Reading Multiple Excel Files in Bulk into KNIME Update
- A Great Helper Node for Time Series Analysis in KNIME
- Examples of How to Use Loops in KNIME
- More on Loops in KNIME - Several Ways to Get the Same Result
- Loops - How to Split Data into Multiple Output Files
- Loops Recursion in KNIME
- Webscraping with KNIME
- Webscraping with KNIME - Financial Data
- Scripting - How to Use Python in KNIME
- Python in KNIME - Further Examples
- Hyperparameter Optimization in KNIME - Data Preparation
- Hyperparameter Optimization for Machine Learning Models Using Loops in KNIME
- Feature Selection in KNIME
- Machine Learning Prediction Process
- KNIME Logout
-
Older Videos KNIME Version Before 4.3
About this
video
Data preparation, data cleaning, data preprocessing (whatever you want to call it) is quite often the most tedious and time-consuming work in the data science/data analysis area. Especially if we are short of time and want to deliver crucial data analysis insights to our audience.
KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.
In this course, we will learn the efficient ways to import multiple files into KNIME, loops, web scraping, scripting (using Python code in KNIME), hyperparameter optimization, and feature selection. Also, learn basic machine learning workflows and helpful nodes for this in KNIME.
By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.
All the resources and support files for this course are available at https://github.com/PacktPublishing/Data-science-and-Data-preparation-with-KNIME
- Publication date:
- September 2021
- Publisher
- Packt
- Duration
- 4 hours 0 minutes
- ISBN
- 9781801073288